CUSTOMER SEGMENTATION ANALYSIS OF ELECTRONIC GOODS A MACHINE LEA

CUSTOMER SEGMENTATION ANALYSIS OF ELECTRONIC
GOODS
A MACHINE LEARNING APPROACH - Machine Learning and
Optimization
Abstract
There seems to be a lot of competition among competing firms
to attract new customers and hold on to existing ones as a result
of the creation of numerous competitors and entrepreneurs.
Because of the above, providing excellent customer service is
necessary regardless of the size of the company. Furthermore,
the capacity of any company to comprehend the requirements of
each of its clients will enhance client assistance in offering
focused client services and creating unique client care
strategies. Structured customer service makes it possible to have
this understanding. Customers in each segment are similar in
terms of market characteristics.
Introduction
Data mining algorithms are now frequently used to uncover
vital and strategic information that is concealed in
organizational data as a result of growing company competition
and the accessibility of large-scale historical data. (Rajaraman,
2011) Data mining is the process of removing logical
information from a dataset and presenting it for decision
support in a way that is easily understandable by humans. Data
mining techniques set apart disciplines like statistics, AI,
machine learning, and data systems. Applications for data
mining range from biology to weather forecasting, fraud
detection, financial research, and customer insights, among
others. The main goal of this article is to use data mining to
find client segments in a commercial enterprise.

These differences are based on factors that directly or indirectly
affect the market or business such as product preferences or
expectations, location, behavior and so on. The importance of
customer segmentation includes, inter alia, the ability of a
business to customize market plans that would be appropriate
for each segment of its customers.Support for business
decisions based on risky environments such as credit
relationships with its customers (Rogers, 2016); Identify
products related to individual components and how to manage
demand and supply power; Interdependence and interaction
between consumers, between products, or between customers
and products are revealed, which the business may not be aware
of; The ability to predict customer declines, and which
customers are likely to have problems and raise other market
research questions and provide clues to find solutions. Buried in
a database of integrated data proved to be effective for
detecting subtle but subtle patterns or relationships.
This mode of learning is classified under supervised learning.
Integration algorithms include the KMeans algorithm, K-nearest
algorithm, sorting map (SOM), and more (Morrisson, 2014).
These algorithms, without prior knowledge of the data, are able
to identify groups in them by repeatedly comparing input
patterns, as long as static aptitude in training examples is
achieved based on subject matter or process. Each set has data
points that have very close similarities but differ greatly from
the data points of other groups.
Results:
Data frame output
Cash Advance vs Balance

Customer ID vs Balance Frequency
Balance vs Purchases Observation
Installment Purchase Vs Cash Advance
Purchases Vs Cash Advance Frequency
Credit Limit Vs Payments
Machine Learning K means Clustering Output:
Conclusion
Regardless of the information provided, the results provide a
practical opportunity.For retailers to carry out marketing
campaigns or similar segmentation consumers. Despite the
usefulness of the status quo analysis, there are many ways to
improve and grow. While there was a motive to maintain it. The
number of functions is small, and another function is added in
consideration of timeliness. Consumer percentages provide a
clearer indicator of whether a particular purchase decision has
been made. Profiles are more common now than the store's

past. In a similar way find a way to cluster your customers
faster (for example, one or two visits are more likely) (Mattson,
2014). Not only provide insights into the evolutionary aspects
of (3 or more) Although it is clustering, there is also a potential
outflow of customers. Same analysis using many other
clustering algorithms such as k- Means clustering or deep
learning provides insight into stability of cluster formation.
References:
1. Mattson, M. P. (2014), ‘Superior pattern processing is the
essence of the evolved human brain’, Frontiers in Neuroscience
8, 265.
2. Morrison, C., Gruenewald, P. J., Freisthler, B., Ponicki, W.
R. & Remer, L. G. (2014), ‘The economic geography of medical
cannabis dispensaries in california’, International Journal of
Drug Policy 25(3), 508 – 515. URL:
http://www.sciencedirect.com/science/article/pii/S09553959130
02387
3. Rajaraman, A. & Ullman, J. D. (2011), Mining of Massive
Datasets, Cambridge University Press, New York, NY, USA.
4. Rogers, S. & Girolami, M. (2016), A First Course in Machine
Learning, Second Edition, Chapman & Hall/CRC
Prepare a PowerPoint about this company's ethical dilemma and
resulting ethical failure, according to the following instructions.
Sources are provided to assist you getting started (click
company name link). You will need to further research the
company as well as applicable ethical frameworks and related
law in your text and required readings.
The following resources will also assist your PowerPoint.
· What is Ethical Dilemma?
· Checklist of guidelines when you face ethical dilemmas
1. Create a 12
· Title slide with your name, course, date, school, title of

presentation;
· Agenda slide - This lists the key points covered in the PPT;
· Content slides containing bullet points information with
illustrations, diagrams, pictures, graphics etc., as appropriate to
the slide's content;
· Speaker's notes on each slide - either text presented in the
Speaker Notes section at the bottom of the slides or Audio
through your Voice speaking (or both); (Note: Speaker's Notes
are not duplication of the text on the slides. They are
explanatory narrative.)
1. Identify the company you selected Topic Purdue Pharma -
opioid crisis, deceptive marketing.
· explain the company and its industry;
· provide the factual background of the problem; and
· clearly state the ethical dilemma presented by the situation.
There should be only ONE ethical dilemma. The company had
two choices: the act it chose and an alternative it did not do.
3. Identify and define at least oneethical framework that the
company apparently employed in making its decision. Note --
Not "Should have used." It is not acceptable to say it did not act
ethically or did not use a framework. Analyze it. Frameworks
include utilitarianism, free market ethics, deontology, virtue
ethics , etc., covered in your course readings.
4. Then, identify and define at least oneethical framework that
the company should have used when the problem arose, and
explain how to apply it for them to have reached a better result
than what actually happened. Be clear.
5. Identify and explain measures the company should implement
to avoid this type of problem in the future.
6. Within your discussion include whether the company had a
code of ethics or policy that seemed to apply to the situation,
and if so, what went wrong with that?
7. Explain what business leadership in any company can learn
from this situation.
8. Have a conclusion that wraps up the key points.
9. Include a Reference slide with at least seven (7) credible

sources listed in APA format. (NOTE: Sources must be cited in
slides and speaker's notes for direct quotes, specific facts and
graphics, same as in a paper, per APA format. If you are doing
audio speaker notes, you would mention the source in your
speaking narrative.) (Reminder; we are using APA 7th Ed.
format.)
10. Your PPT should have a professional slide background and
graphics on each slide. (See rubric)
Prepare a PowerPoint about this company's ethical dilemma and
resulting ethical failure, according to the following instructions.
Sources are provided to assist you getting started (click
company name link). You will need to further research the
company as well as applicable ethical frameworks and related
law in your text and required readings.
The following resources will also assist your PowerPoint.
· What is Ethical Dilemma?
· Checklist of guidelines when you face ethical dilemmas
1. Create a 12
· Title slide with your name, course, date, school, title of
presentation;
· Agenda slide - This lists the key points covered in the PPT;
· Content slides containing bullet points information with
illustrations, diagrams, pictures, graphics etc., as appropriate to
the slide's content;
· Speaker's notes on each slide - either text presented in the
Speaker Notes section at the bottom of the slides or Audio
through your Voice speaking (or both); (Note: Speaker's Notes
are not duplication of the text on the slides. They are
explanatory narrative.)
1. Identify the company you selected Topic Purdue Pharma -
opioid crisis, deceptive marketing.
· explain the company and its industry;
· provide the factual background of the problem; and
· clearly state the ethical dilemma presented by the situation.
There should be only ONE ethical dilemma. The company had

two choices: the act it chose and an alternative it did not do.
3. Identify and define at least oneethical framework that the
company apparently employed in making its decision. Note --
Not "Should have used." It is not acceptable to say it did not act
ethically or did not use a framework. Analyze it. Frameworks
include utilitarianism, free market ethics, deontology, virtue
ethics , etc., covered in your course readings.
4. Then, identify and define at least oneethical framework that
the company should have used when the problem arose, and
explain how to apply it for them to have reached a better result
than what actually happened. Be clear.
5. Identify and explain measures the company should implement
to avoid this type of problem in the future.
6. Within your discussion include whether the company had a
code of ethics or policy that seemed to apply to the situation,
and if so, what went wrong with that?
7. Explain what business leadership in any company can learn
from this situation.
8. Have a conclusion that wraps up the key points.
9. Include a Reference slide with at least seven (7) credible
sources listed in APA format. (NOTE: Sources must be cited in
slides and speaker's notes for direct quotes, specific facts and
graphics, same as in a paper, per APA format. If you are doing
audio speaker notes, you would mention the source in your
speaking narrative.) (Reminder; we are using APA 7th Ed.
format.)
10. Your PPT should have a professional slide background and
graphics on each slide. (See rubric)
Machine learning based Customer Segmentation
Introduction
Since it is easy for customers to watch videos and live
streaming programs in mobile phones, several customers are

making use of huge amount of data with the support of faster
means of 4G network system. Moreover, the huge collection of
diverse apps in mobile phones also consume a big space of the
memory. Because of this, the data service providers also
increase for mobile providers [9]. In addition, as more apps for
iOS and Android devices are advanced, new customers
download new apps and use them, which in advance increases
their data usage [1]. As of increasing mobile data prices, huge
numbers of subscriber’s churn from one provider to a different
one in pursuit of enhanced taxes. They too churn providers to
obtain assistances for validating up with a new carrier, such as
receiving a free or deeply discounted phone [10]. In addition,
the lower signup fees associated with prepaid mobile services
also encourages customers to churn.
“The ability of mobile customers to keep their existing mobile
numbers through the Wireless local number portability (WLNP)
reduces barriers to churning within the industry, which is a
major problem for companies in the telecommunications
industry [2]. Because of the likelihood of customers to change
providers, the deals that telecommunication companies offer
may differ based on the needs of individual customers and their
wiliness to pay for particular services. ID Mobile Ireland are the
company that is the basis for the work performed in this paper.
They are a start-up telecommunications provider in the Republic
of Ireland. The company differentiates itself in the competitive
Irish market by separating the mobile tariff from the handset
[7]. This allows customers the flexibility to enter or leave a 12,
18 or 24-month contract without penalty, and purchase a new
handset every three months, should they wish to do so once the
previous handset cost is fully paid off”.
Additionally, as the customer is not tied down into an extended
contract where the cost of the phone is subsidized by the tariff
price, customers may change their tariff call, text and data
allowances every month, to suit their individual needs, allowing
them more control over their account charges. They could, for
example, increase their call minutes bundle amount for the

month of December should they envisage making more calls
during this peak holiday period. The company has access to a
wide range of data, with the prospect to capture even more data,
growing at a rapid rate. The data that the company can access
are currently not being used to their full potential as a means of
understanding the customers that are served, their sale patterns,
the potential fraud risks, and churn patterns [8]. In this paper,
the goal is to collect, clean, categorize, and gain insight from a
large dataset spanning 16 months of Bill Pay customer account
data that contains 26717 rows and 86 columns of attributes. The
data also contain an additional 11 columns comprised of
formula derived values or classes used to categorize the data.
The primary aim of this effort is to better meet customer needs,
improve customer satisfaction, developing customer loyalty to
the brand as a means of improving customer retention.
“The initial step in carrying out this effort was to acquire the
relevant data to generate the various reports, using the attributes
available, and to cross check the results in the production
customer care system as a means of confirming the accuracy of
the data. This verification step proved to be very important
because as several tables of data where combined, erroneous
results occurred [4]. This meant that separate reports had to be
created because all of the data could not be in one report due to
the database tables not containing the required logic to be
joined together or the report data outputted exceeding the
current maximum capable by the system, which was 70,000
cells of data”.
Literature Review
The variables that are generally used for market segmentation
are Demographic, socioeconomic, and geographic
characteristics of the customers. A very useful technique for
behavioral-based data mining method in the RFM analysis,
which involves the extraction of customer profiles by using a
few criteria, which reduces the complexity of analysis [5]. “In
RFM analysis, customer data are classified by Recency (R),
Frequency (F) and Monetary (M) variables. It has been noted

that RFM enables the practitioners to observe customer
behavior, as well as to segment customers in order to determine
immediate customer value [5]. It should also be noted that using
decision rules algorithms for the purpose of customer
segmentation may result in an efficient evaluation of a
segmentation plan [3]. Decision trees can be identified into sets
of if-then rules, which means that they can be used to solve a
variety of problems, such as customer segmentation and
customer churn prediction. In fact, many researchers have used
this method to study customer segmentation”.
A customer satisfaction survey can be used to construct a
customer segmentation system based on demographic variables
and even customer reviews [1]. Researchers have provided ideas
about modelling customer satisfaction using unstructured data
with a Bayesian approach. They explain that the transformation
of unstructured data taken from customer’s reviews into a semi -
structured form associated with each aspect reflecting the
frequency counts for positive, negative, and neutral sentiments.
One assumption of this model is that the rating of each aspect is
based on a particular combination of the positive, neutral, and
negative sentiments of that particular aspect. The result is that
the overall aspect rating depends upon how many times an
aspect has been associated with positive, neutral and negative
sentiments in a single customer review. Furthermore, there is
also an overall rating that is assigned to each review by the
contributor [6].
Research Methodology
The following project is based on identifying potential
customers for a particular product. This project will be
implemented using the python programming language. For
machine learning techniques we will use K-means clustering
with ANFIS. The algorithm used for the project is very
essential. Segmentation is the process of dividing customers
into various groups for targeted selling. This data analytics
project can help sellers a lot in many ways. The sellers can
know about the customer’s mentality hence increasing the

market for the sellers. The algorithm is custom developed for
the features trained and the data management is managed
through SQL.
A. K-Means Clustering
“K-means clustering algorithm is one of the clustering
algorithms based on division. It adopts a heuristic iterative
process to re-divide data objects and re-update cluster centers.
The basic idea of the algorithm is: suppose a set with element
objects and the number of clusters to be generated [2]. In the
first round, a sample element is randomly selected as the initial
cluster center [6], and the distance between other sample
elements and the center point is analyzed the clusters are
respectively divided according to the distance. In each of the
following rounds, the iterative operation of the above steps is
continuously performed, and the average value of the element
objects obtained this time is taken as the center point of the
next round of clustering until the condition that the clustering
center point no longer changes in the iteration process is met.
The specific processing steps are as follows”:
Fig. 1 K-Means Algorithm
B. Adaptive Neuro-Fuzzy Inference System (ANFIS)
“Fuzzy logic and neural network are widely used in prediction
problems. ANFIS is a useful technique based on fuzzy logic and
neural network approaches [27]. It takes the advantages of both
fuzzy set theory in applying rule-based systems and neural
networks in automatic learning from data. A fuzzy inference
system in the ANFIS technique consists of if–then rules,
membership functions, inference mechanism (called fuzzy
reasoning), and couples of input–output. In Figure 2, a structure
of a fuzzy inference system is presented. As seen from this
figure, in the first step, the inputs are fuzzified to produce their
degrees of truth. In the second step, the degree of truth of the
consequents is obtained by combining this information through
inference rules. In the last step, final output is obtained by
defuzzification”.

Fig. 2 A structure of a fuzzy inference system for revealing
customer satisfaction
References
1. Dullaghan, C., & Rozaki, E. (2017). Integration of machine
learning techniques to evaluate dynamic customer segmentation
analysis for mobile customers. arXiv preprint
arXiv:1702.02215.
2. Ezenkwu, C. P., Ozuomba, S., & Kalu, C. (2015). Application
of K-Means algorithm for efficient customer segmentation: a
strategy for targeted customer services.
3. Smeureanu, I., Ruxanda, G., & Badea, L. M. (2013).
Customer segmentation in private banking sector using machine
learning techniques.
4. Monil, P., Darshan, P., Jecky, R., Vimarsh, C., & Bhatt, B. R.
(2020). Customer Segmentation Using Machine
Learning. International Journal for Research in Applied Science
and Engineering Technology (IJRASET), 8(6), 2104-2108.
5. Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018,
December). Customer segmentation using K-means clustering.
In 2018 international conference on computational techniques,
electronics and mechanical systems (CTEMS) (pp. 135-139).
IEEE.
6. Hiziroglu, A. (2013). Soft computing applications in
customer segmentation: State-of-art review and critique. Expert
Systems with Applications, 40(16), 6491-6507.
7. Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining
techniques in CRM: inside customer segmentation. John Wiley
& Sons.
8. Hung, P. D., Lien, N. T. T., & Ngoc, N. D. (2019, March).
Customer segmentation using hierarchical agglomerative
clustering. In Proceedings of the 2019 2nd International
Conference on Information Science and Systems (pp. 33-37).
9. Ozan, Ş. (2018, September). A case study on customer
segmentation by using machine learning methods. In 2018
International Conference on Artificial Intelligence and Data

Processing (IDAP) (pp. 1-6). IEEE.
10. Wu, S., Yau, W. C., Ong, T. S., & Chong, S. C. (2021).
Integrated churn prediction and customer segmentation
framework for telco business. IEEE Access, 9, 62118-62136.
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